Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
Graph neural network for traffic forecasting: The research progress
Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
Predicting urban region heat via learning arrive-stay-leave behaviors of private cars
Urban region heat refers to the extent of which people congregate in various regions when
they travel to and stay in a specified place. Predicting urban region heat facilitates broad …
they travel to and stay in a specified place. Predicting urban region heat facilitates broad …
Spatio-temporal graph mixformer for traffic forecasting
Traffic forecasting is of great importance for intelligent transportation systems (ITS). Because
of the intricacy implied in traffic behavior and the non-Euclidean nature of traffic data, it is …
of the intricacy implied in traffic behavior and the non-Euclidean nature of traffic data, it is …
A new approach to COVID-19 data mining: A deep spatial–temporal prediction model based on tree structure for traffic revitalization index
The outbreak of the COVID-19 epidemic has had a huge impact on a global scale and its
impact has covered almost all human industries. The Chinese government enacted a series …
impact has covered almost all human industries. The Chinese government enacted a series …
Deep neural networks for spatial-temporal cyber-physical systems: A survey
Cyber-physical systems (CPS) refer to systems that integrate communication, control, and
computational elements into physical processes to facilitate the control of physical systems …
computational elements into physical processes to facilitate the control of physical systems …
VDGCNeT: A novel network-wide Virtual Dynamic Graph Convolution Neural network and Transformer-based traffic prediction model
We address the problem of traffic prediction on large-scale road networks. We propose a
novel deep learning model, Virtual Dynamic Graph Convolution Neural Network and …
novel deep learning model, Virtual Dynamic Graph Convolution Neural Network and …
Two-scale based energy management for connected plug-in hybrid electric vehicles with global optimal energy consumption and state-of-charge trajectory prediction
Traffic conditions of the road network significantly affect the energy consumption (EC) of plug-
in hybrid electric vehicles (PHEVs). However, they are not effectively used in existing energy …
in hybrid electric vehicles (PHEVs). However, they are not effectively used in existing energy …
Mfdgcn: Multi-stage spatio-temporal fusion diffusion graph convolutional network for traffic prediction
Z Cui, J Zhang, G Noh, HJ Park - Applied Sciences, 2022 - mdpi.com
Traffic prediction is a popular research topic in the field of Intelligent Transportation System
(ITS), as it can allocate resources more reasonably, relieve traffic congestion, and improve …
(ITS), as it can allocate resources more reasonably, relieve traffic congestion, and improve …
[PDF][PDF] Continuous Sign Language Recognition Based on Spatial-Temporal Graph Attention Network.
Q Guo, S Zhang, H Li - CMES-Computer Modeling in …, 2023 - cdn.techscience.cn
Continuous sign language recognition (CSLR) is challenging due to the complexity of video
background, hand gesture variability, and temporal modeling difficulties. This work proposes …
background, hand gesture variability, and temporal modeling difficulties. This work proposes …